Bayesian method for solving the problem of multicollinearity in regression

The popular method of estimation in regression, Ordinary Least Squares (OLS) often displays inefficiency especially with large variances and wide confidence intervals thereby making precise estimate difficult when there is strong multicollinearity. Bayesian method of estimation is expected to improve the efficiency of estimated regression model when there is relevant prior information and belief of situation being modelled is available. This study however provided an alternative approach to OLS when there is almost perfect multicollinearity while its performance were compared with the aid of simulation approach to OLS estimator. Results of the simulation study indicate that with respect to Mean Squared Error (MSE) criterion and other criteria, the proposed method perform better than OLS.